Cloud classification using local multiresolution texture descriptors

In this work a technique for cloud classification from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infra-red Imager) imagery is presented. It is based on the segmentation of the multispectral images using local texture information. The goal is to study, how local texture measures, can distinguish different cloud genres, and to develop a system based on multiresolution analysis that combines spectral and textural image characteristics in a multiscale model, to facilitate the automated interpretation of satellite cloud images. To evaluate the performance of this analysis we applied the methodology to a brodatz database and to MSG images. A series of 168 infrared and visible satellite images from the geostationary satellite METEOSAT were employed. Three different texture feature sets (a total of 24 features) were extracted using the cooccurrence matrices in the wavelet domain, and the fuzzy c-means clustering method is used for the classification. The approach indicated very good performance in classification experiment. The proposed feature extraction and classification scheme achieved high accuracies, with an overall classification accuracy of 97.53%.